{"id":"W4413138754","doi":"10.1109/tbme.2025.3599457","title":"DINOMotion: Advanced Robust Tissue Motion Tracking With DINOv2 in 2D-Cine MRI-Guided Radiotherapy","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Radiation therapy; Tracking (education); Match moving; Computer vision; Motion (physics); Computer science; Medical imaging; Magnetic resonance imaging; Medical physics; Artificial intelligence; Biomedical engineering; Radiology; Medicine; Psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001035668,0.0002606458,0.0002968794,0.0004570379,0.00008313087,0.00002682928,0.0001507046,0.00009340881,0.000220681],"category_scores_gemma":[0.000001498679,0.0002416029,0.00006683711,0.0008432394,0.00006781251,0.0002274828,9.09755e-7,0.0004085332,0.000001304821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002188205,"about_ca_system_score_gemma":0.00003415855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004937504,"about_ca_topic_score_gemma":0.000004374746,"domain_scores_codex":[0.9987791,0.0000195156,0.0003287394,0.0003533723,0.0001954692,0.000323824],"domain_scores_gemma":[0.999508,0.00006745302,0.00004603634,0.000250463,0.00003320124,0.00009480923],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005596865,0.0003639851,0.0001196516,0.00004285949,0.0001083697,0.00000795568,0.0001201119,0.5550566,0.06960474,0.0006137824,0.00004495625,0.373861],"study_design_scores_gemma":[0.007822507,0.0007743721,0.001378036,0.001394008,0.00008424555,0.00002236714,0.0001143212,0.2488268,0.7090978,0.0001986174,0.02899486,0.001292101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007930346,0.00009339402,0.9902381,0.0005429109,0.0003542004,0.0003644848,0.00001486219,0.0002981128,0.0001636361],"genre_scores_gemma":[0.915094,0.00007707564,0.08417632,0.00005461101,0.0001201799,0.0001558055,0.00001211051,0.00004848284,0.0002614673],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9071636,"threshold_uncertainty_score":0.9852281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007803193714311517,"score_gpt":0.2584112082447566,"score_spread":0.2506080145304451,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}